IIBA CBDA (Certification in Business Data Analytics) May 30-31, June 6-7, 13-14, 2020

Duration & Timings: 

6 Virtual Online Sessions  May 30-31, June 6-7, 13-14, 2020

(5.00 PM to 11:45 PM India Time)

Data-focused organizations perform better and making the most of the data is key in making sound business decisions. Your strong business analysis skills coupled with the knowledge, competencies and experience performing business analytics activities are in high demand. 

 

A recent article ranked business analysis among the most in-demand skills of 2019, along with analytical reasoning. Certification in Business Data Analytics is the key to help you prepare for these in-demand skills and to remain on top of industry trends. 

Earning this certification informs employers of your passion for and competencies performing business analysis on analytics initiatives. The certification helps identify skilled business data analytics professionals to organizations seeking these in-demand skills. 

a. Introduction to Business Data Analytics (BDA) 

b. Relationship between Business Analysis and Business Data Analytics 

c. Understanding terminology:

    BDA, Data Science, AI, Machine Learning, Big Data etc. 

    Supervised and Unsupervised Machine Learning 

d. Types of Business Data Analytics methods 

a. Understanding the Business Data Analytics Life Cycle 

b. Identify the Research Questions 

    Defining the business problem(s) 

    Articulating the business problem as an analytical problem 

    Defining success KPIs 

    Building hypothesis, and framing the research question(s) 

    Type I and Type II errors 

     Using DMN (Decision Model and Notation) to build a Decision Requirements Model 

c. Source Data 

    Types of data 

    Defining data requirements 

    Developing a Data Collection Plan 

    Identifying data sources 

    Collecting data 

    Understanding data modeling 

d. Analyze Data 

    Machine Learning Fundamentals 

        - Supervised Learning Algorithms 

        - Unsupervised Learning Algorithms

    The Concept of “Over-fitting” 

        - Bias Error and Variance Error 

        - Addressing “over-fitting” 

    Data Preparation: Pre-processing data 

        - Formatting data 

        - Cleaning data 

        - Sampling data 

    Data Preparation: Transforming data (Feature Engineering)

    Testing and selecting algorithms 

    Building models 

    Evaluating models 

e. Interpret and Report Results  

    Understanding the Stakeholder Engagement Life Cycle 

    Data Visualization vs Data Storytelling 

    Understanding commonly used charts and plots 

    Understanding Data Storytelling 

f. Use Results to Influence Business Decision-Making 

    Making recommendations 

    Developing the Change Implementation Plan 

    Performing business validation of the model 

    Deploying the analytics solution 

    Managing the business change 

g. Manage the model life cycle 

3. INSTITUTIONALIZING BUSINESS DATA ANALYTICS 

    Business Data Analytics challenges 

    Building a Data Strategy 

    Understanding techniques to build a Data Strategy 

    Understanding Data Management